Traffic modeling and classification using packet train length and packet train size

Authors:
Dinil Mon Divakaran;Hema A. Murthy;Timothy A. Gonsalves
Affiliations:
Department of Computer Science and Engineering, Indian Institute of Technology, Madras, Chennai;Department of Computer Science and Engineering, Indian Institute of Technology, Madras, Chennai;Department of Computer Science and Engineering, Indian Institute of Technology, Madras, Chennai
Venue:
IPOM'06 Proceedings of the 6th IEEE international conference on IP Operations and Management
Year:
2006

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Abstract

Traffic modeling and classification finds importance in many areas such as bandwidth management, traffic analysis, traffic prediction, network planning, Quality of Service provisioning and anomalous traffic detection. Network traffic exhibits some statistically invariant properties. Earlier works show that it is possible to identify traffic based on its statistical characteristics. In this paper, an attempt is made to identify the statistically invariant properties of different traffic classes using multiple parameters, namely packet train length and packet train size. Models generated using these parameters are found to be highly accurate in classifying different traffic classes. The parameters are also useful in revealing different classes of services within different traffic classes.